Conversely, CC-DBP and NYT-FB contain dirtier sentences which mean a high probability of incurring wrong labeling.

for instance, T-REX has well-written textual mentions, because the sentences are extracted from Wikipedia.


Formally, given is a new dense representation of w ij which encodes also the information of the whole sentence.

siamese neural networks (Bromley et al., 1993) are well suited to this task because they are specifically designed to compute the similarity between two instances.


This kind of neural network has been used in both computer vision (Koch et al., 2015) and natural language processing (Mueller and Thyagarajan, 2016;Neculoiu et al., 2016) in order to map two similar instances close in a feature space.

the works in (Gladkova et al., 2016;Vylomova et al., 2016) explore the use of word vectors to model the semantic relations.


In order to avoid this situation, and to prevent overfitting, we apply l 2 regularization to the noise model.

3  We further investigate the performance of the proposed approach on instance-dependent label noise by flipping each class labels with different noise percentages as shown in Fig.


The results of our analysis imply that early in training, representing part of speech is the natural way to get initial high performance.

3 If the LM begins by maximizing mutual information with input, because the input is identical for the LM and tag models it may lead to these similar initial representations, followed by a decline in similarity as the compression narrows to properties specific to each task.


Note that topic and UDP POS both apply to the same enwikipedia corpus, but PTB POS and SEM use two different unaligned sets from the GMB corpus.

we ask: is our first conceptual shift (to SVCCA) necessary?


Moreover, the statistics only need to be computed once and can be re-used for training many different smaller-corpus RNNLMs.

in the table, the numbers in the column headings indicate the token count of the sequential corpus used to train the regularized methods.


In this case, we train different KB embeddings for different percentages of overlapped relations, and then apply the embeddings into the constraints.

we then discard named  entities in text corpus if they are not shown in KB, so that we can directly test the influence of our KB constraint model.


The data is built by aligning Wikidata (Vrandečić, 2012) relations with NYT corpus, as a result of 99 possible relations.

this may be because the way that we put constraints only over sampled sentences in a batch may hurt the regularization of decoder, since sampled unique pairs may be less than sample sentences.


Based on the decoder, we make predictions for each unique entity pair.

we also thank the anonymous reviewers for their valuable comments and suggestions that help improve the quality of this manuscript.


As shown in Figure 1, in the KB, we have entities Pink Floyd, Animals, etc., with some existing relations notable work and has member in the KB.

we use this dataset to compare with previous work directly (Marcheggiani and Titov, 2016).


Also, the errors introduced by round-trip translation are relatively clean, but they represent only a subset of the domain of real-world errors.

we show that models trained exclusively on minimally filtered English wikipedia revisions can already be valuable for the GEC task.


In Table 4, we show an example of iterative decoding in action.

each system may have advantages on specific domains.


Hence, we report results using only this scheme.

the model trained on all data performs best on the JFLEG set, which has a different distribution of errors relative to CoNLL-2014 (Napoles et al., 2017).


found that large-data LSTM behavior reflected this island constraint, with attenuated wh-licensing interactions for complex NPs like (11-b)-(11-c) and for analogous complex NPs involving subject extractions.

the results of this experiment can be seen in Figure 6.


These materials paint a slightly more optimistic picture than the results of Section 4.4 for the RNNG's ability to propagate a gap expectation from a filler down one level of clausal embedding.

we applied the same method, adapting wilcox et al.


A word that achieves minimal loss after swapping with P AD is then selected as the word to be replaced.

furthermore, by iterative adversarial training using our black-box RL agent, we can significantly improve the robustness of the dialogue system.


This leads to several difficulties including 1) How to lead the target agent to a bad state and 2) how to force the target agent to make a wrong decision.

in a competitive negotiation dialogue setting, two agents are negotiating with each other over a set of items.


Also, our agent achieves a relatively high positive advantage rate at 84.45% and 69.35% respectively.

an adversarial sentence or example is not enough to conduct attack in dialogue sys-tems.


First, evaluating the effect of changing the KG on the score of the target fact (ψ(s, r, o)) is expensive since we need to update the embeddings by retraining the model on the new graph; a very time-consuming process that is at least linear in the size of G. Second, since there are many candidate facts that can be added to the knowledge graph, identifying the most promising adversary through search-based methods is also expensive.

although CRIaGE is primarily applicable to multiplicative scoring functions [Nickel et al., 2011, Socher et al., 2013, Trouillon et al., 2016, these ideas apply to additive scoring functions [Bordes et al., 2013a, Wang et al., 2014, Lin et al., 2015, Nguyen et al., 2016 as well, as we show in appendix a.3.


Recently, a great attention has been paid on neural networks.

the comparisons show that the proposed model has a greater generalization ability.


Semantic role labeling (SRL) is a task to recognize all the predicate-argument pairs of a given sentence and its predicates.

we leverage a stacked BiLSTM network LST M e to be our encoder.


We measure the speed in terms of sentences per second.

in the case of BiLSTM-based sequence tagging parsers, for a given word w t , the output label as encoded by Gómez-Rodríguez and Vilares (2018) only reflects a relation between w t and w t+1 .


Distances can be used for sequence tagging, providing additional information to our base encoding (Gómez-Rodríguez and Vilares, 2018) The proposed auxiliary tasks provide different types of contextual information.

we propose to learn this decomposed label space through a multitask learning setup, where each of the subspaces is considered a different task, namely task N , task C and task U .


Tables 3, 4 and 5 compare our parsers against the state of the art on the PTB, CTB and SPMRL test sets.

we will evaluate the impact of the different methods intended to perform structured inference ( §3.4).


Named entity recognition (NER) is a common task in Natural Language Processing (NLP), but it remains more challenging in Chinese because of its lack of natural delimiters.

(2018c) leverage character-level BiL-STM to extract higher-level features from crowdannotations.


Both on the development and test sets, our system records significant gain, in comparison to Teran-ishi+17:+ext, on Simple coordination sentences.

this means that the model produces a score based on the end of the left conjunct and the beginning of the right conjunct.


Therefore, it can mistakenly segment coordinations when false sub-coordinators appear in a sentence.

we categorize sentences into the following four groups 5 .


3 Japanese PASA and ENASA Japanese predicate (event-noun) argument structure analysis is a task to extract arguments for certain predicates (event-nouns) and assign three case labels, NOM, ACC and DAT .

we call these nouns that refer to events event-nouns, for example, a verbal noun (sahen nouns) such as houkoku "report" or a deverbal noun (nominalized forms of verbs) such as sukui "rescue."


No dependency relations between the predicate (event-noun) and argument candidates.

in NTC 1.5, if there is a predicate phrase, such as "verbal noun + suru," suru is annotated as a predicate word.


However, Multi-ALL+DEP compared unfavorably with Multi-ALL even though it was the best PASA architecture.

our single model is based on an end-to-end approach (Zhou and Xu, 2015;ouchi et al., 2017;Matsubayashi and Inui, 2018).


(2017) proposed an end-to-end model based on the model using eight-layer bi-directional long shortterm memory (LSTM) proposed by Zhou and Xu (2015) and considered the interaction of multiple predicates simultaneously using a Grid RNN.

the model improved the performance of arguments that have no syntactic dependency with predicates and achieved a state-of-the-art result on Japanese PASA.


According to the description of the dependency tree in Section 3.1, leaf nodes are isolated discourse units and no other discourse units depend on them.

we utilize five available fake news datasets in this study.


In total, we have 3360 fake and 3360 real documents.

the less value of this property is, the more coherent a document is likely to be.


The position of s j in the original sequential order is simply j 2 .

intuitively, the less the value of Property 2 for a document is, the more coherent that document should be.


We report model performance on the test-set using automatic metrics as well as human evaluation.

the detection conditions of the discourse structure, and the sequence of operations for generating a new sentence pair from it.


Sentence fusion is challenging because it requires understanding the discourse semantics between the input sentences.

* Work done during internship at Google AI.


Anaphora and verb phrase coordination are more challenging, but still require matching of the same noun (the named entity or the subject).

thadani and McKeown (2013) constructed 1,858 examples from summarization tasks.


If none of the specificity metrics are included, topic relevance scores improve.


A similar trend can also be observed in Table 8, where LOG-CaD could generate the locational expressions such as "philippines" and "british" given the different contexts.

then, we analyze the impact of global context under the situation where local context is unreliable.


While their method use local context for disambiguating the meanings that are mixed up in word embeddings, the information from local contexts cannot be utilized if the pre-trained embeddings are unavailable or unreliable.

each entry in the dataset consists of (1) a phrase, (2) its description, and (3) context (a sentence).


As future work, we plan to modify our model to use multiple contexts in text to improve the quality of descriptions, considering the "one sense per discourse" hypothesis (Gale et al., 1992).

to the existing methods for non-standard English explanation (Ni and Wang, 2017) and definition generation (Noraset et al., 2017; Gadetsky et al., 2018), our model appropriately takes important clues from both local and global contexts.


In what follows, we explain existing tasks that are related to our work.

the target of paraphrase acquisition are words/phrases with no specified context.


Very little work adequately exploits unannotated data-such as discourse markers between sentences-mainly because of data sparseness and ineffective extraction methods.

one of the most popular frameworks aims to induce sentence embeddings as an intermediate representation for predicting relations between sentence pairs.


For instance, similarity judgements (paraphrases) or inference relations have been used as prediction tasks, and the resulting embeddings perform well in practice, even when the representations are transfered to other semantic tasks (Conneau et al., 2017).

out of the 42 single word PDTB markers that precede a comma, 31 were found by our rule.


For the local evaluation metric, we mainly consider Vocab@-3% and Vocab@-5%.

nLI datasets involve more complicated reasoning and interaction, which requires a thousand-level vocabulary.


In this paper, we provide a more sophisticated variational vocabulary dropout (VVD) based on variational dropout to perform vocabulary selection, which can intelligently select the subset of the vocabulary to achieve the required performance.

we are interested in understanding how the end-task classification accuracy is related to the vocabulary size and what is the minimum required vocabulary size to achieve a specific performance.


Loss function Ψ(•): First, to improve readability, we introducev w as a short notation of τ (V , w), namelyv w = τ (V , w).

we adopted Adam (Kingma and Ba, 2014) as our optimization algorithm to minimize Eq.


One might be surprised the improved results by KVQ-FH since the model sizes of KVQ-FH were relatively very small comparing with the original embeddings.

here, we consider utilizing a squared loss function Ψ lsq , which can be written as the summation of the squared losses over an individual embedding vector e w : where C w is a weight factor for each word.


We would also like to explicitly quantify the uncertainty captured in our framework under different sampling strategies or MCMC-SG methods (e.g., similar to Mc-Clure and Kriegeskorte (2016); Teye et al.

our BNNP consistently outperforms the single task BiLSTM baseline (Kiperwasser and Goldberg, 2016), while outperforming the BiAFFINE parser (Dozat et al., 2017) by up to 3% on Vietnamese and Irish.


Multi-task Learning (MTL) (Caruana, 1997) is an inductive transfer mechanism which leverages information from related tasks to improve the primary model's generalization performance.

for stage-1, the bandit controller iteratively selects batches of data from different tasks during training to learn the approximate importance of each auxiliary task (Graves et al., 2017).


Once this is specified, we aim to find k adversarial distributions {α (1) , ..., α (k) }, such that each α (i) maximizes the distance from originalα but does not change the output by more than .

this is the exception rather than the rule.


The reward function provided by TextWorld is as follows: +1 for each action taken that moves the agent closer to finishing the quest; -1 for each action taken that extends the minimum number of steps needed to finish the quest from the current stage; 0 for all other situations.

we hypothesize that it will cut down on the amount of exploration needed during testing time, theoretically allowing it to complete quests faster; one of the challenges of text adventure games is that the quests are puzzles and even after training, execution of the policy requires a significant amount of exploration.


We further reduce the applied layers to low-level two (Row 5), the above phenomena still holds.

future work includes combining our information aggregation techniques together with other advanced information extraction models for multihead attention .


To address these problems and to unify previous efforts, in a recent work, Jurgens et al.

2018the task of citation intent classification.


For contextual representations, we use ELMo vectors released by Peters et al.

overall, the size of the data for scaffold tasks on the ACL-ARC dataset is about 47K (section title scaffold) and 50K (citation worthiness) while on SciCite is about 91K and 73K for section title and citation worthiness scaffolds, respectively.


We thank Kyle Lo, Dan Weld, and Iz Beltagy for helpful discussions, Oren Etzioni for feedback on the paper, David Jurgens for helping us with their ACL-ARC dataset and reproducing their results, and the three anonymous reviewers for their comments and suggestions.

suggesting the effectiveness the scaffolds in informing the main task of relevant signals for citation intent classification.


This suggests that while the bulk of the signal is mined from the pair-context interactions, there is also valuable information in other interactions as well.

we must determine the frequency in which each triplet appears in each role.


Data Augmentation results for intent and question classification are shown in Table 5.

we perform experiments with multiple augmentation settings for the following classifiers: 1.


Interestingly, incorporating contextual descriptors did not help the prediction of persuasion strategies.

simi-  lar principles might also occur for social identity and scarcity where the use of words such as "we", "our" and "expire", "left" can reveal a lot about the persuasion strategies.


• The principle of Emotion says that making messages full of emotional valence and arousal affect (e.g., describing a miserable situation or a happy moment) can make people care and act, e.g., "The picture of widow Bunisia holding one of her children in front of her meager home brings tears to my eyes..", similar to Sentiment and Politeness used by Althoff et al., (2014) and Tan et al., (2016), and Pathos used by Hidey et al., (2017).

in this context, we did not observe enough instances of them.


We first apply the structural encoder to the input graphs.

the latter recently inspired a graphto-sequence architecture for AMR-to-text generation (Beck et al., 2018).


The trained language models are then applied to the held-out participant's sequence of information units and various language model (LM) features are extracted (Table 2c).

in this task, participants are asked to describe the content of a line drawing of a kitchen scene, where a boy can be seen standing on a stool, trying to reach a cookie jar, while a woman is preoccupied washing dishes.


As shown in Table 5, also in this setting the inclusion of knowledge improves the performance.

when using ELMo with BM we see an improvement in recall.


For example, the token "time" was used on average 0.32 times per document in the Newswire corpus and just over 4 times per document in the THYME corpus (Table 2).

† https://github.com/bethard/anaforatools This section first discusses Chrono's "out-of-thebox" performance on the THYME Evaluation Corpus prior to any code changes.


This phrase is first parsed by the formatted date/time module to identify the HourOfDay "3" and the Minute-OfHour "05" entity.

different domains are expected to differ in their lexicon.


The following sub-sections discuss issues associated with variation in formatted dates, times, and long temporal phrases.

improvements made to Chrono using the THYME Training Corpus lead to a 0.27 and 0.24 increase in precision and recall, respectively, with a 0.26 increase in F1 measure for the Evaluation Corpus (Table 3).


Since Chrono did not consider sentence boundaries, this line break was removed in the preprocessing phase and the "2" that numbers the list item was parsed as a DayOfMonth associated with "December".

chrono specifically includes the period in the span only if there is a period after each letter in strings (e.g.


To train these, we generalize token-wise annotations to sentences such that a sentence is labeled 1 if it contains any evidence tokens, and 0 otherwise.

given the critical role published reports of trials play in informing evidence-based care, organizations such as the Cochrane collaboration and groups at evidence-based practice centers (EPCs) are dedicated to manually synthesizing findings, but struggle to keep up with the literature (Tsafnat et al., 2013).


Concretely, where w α ∈ R 1×d and H a ∈ R d×|a| , denoting hidden size by d and article length by |a|.

the best performing model (and hence current leader) is the variant that uses pretrained, conditional attention, which aligns with the average results in table 2.


To summarize this history, content-aware models (Chen et al., 2016a;Kim et al., 2017) similar to attention models in machine translation (Bahdanau et al., 2014) have been proposed.

the proposed models do not need any manual time-decay function, but learn a timedecay tendency directly by introducing a trainable distance vector, and therefore have good SLU accuracy.


examples of DAs can be found in Table 1.


(Ravi and Kozareva, 2018) bypasses the need for complex networks with huge parameters but its overall accuracy is 4.2% behind our system, despite being 0.2% higher on SwDA.

learning long-range dependencies is a challenge because of noisier and longer path lengths in the network.


This requires the memory reader to learn how to infer relationships across otherwise connected attributes.

the encoder takes a sequence of utterances as input.


The product of first level attention α i , second level attention β ij and third level attention γ ijl gives the final attention score of the value v r ij l in the KB memory.

to store multiple queries, we require 3 levels in our multi-level memory as compared to 2 levels in the other datasets, since they don't have more than one query.


Researchers in this community have attempted to predict the topic directly from the audio signals using phoneme based features.

we evaluate our model on Switchboard (SwBD) corpus (Godfrey et al., 1992) and show that our model supersedes previously applied techniques for topic spotting.


This is another form of doubly-recurrent neural networks.

using a doublyrecurrent architecture, specifically the surface decoder and the ancestor decoder, can improve perplexity scores for top-down word generation over the left-to-right decoder.


It has 2.06M utterances, and we split into training, development and test sets with ratio of 8:1:1.

when we add a new domain embedding s k+1 to this personalization module, the model tends to learn to move this vector to a different part of the vector space such that its easier to distinguish the new domain from all other domains.


In the domain of dialog managers, Mrkšić et al.

for example, for a request such as Set an alarm for tomorrow at 7am, a first step in fulfilling such a request is to identify that the user's intent is to set an alarm and that the required time argument of the request is expressed by the phrase tomorrow at 7am.


We expect the trained model to be such that where SD is the source document, and S i are the the utterances in the conversation.

we have not reported scores for certain multiplicative variants because their performance is significantly worse.


Query Sensitive History Summariser: The history of the prober and responder are passed through a BiGRU to get context sensitive vectors h P j ∈ R 2d and h R k ∈ R 2d for j ∈ [J] and k ∈ [K].

for our simple attention model, the average conicity of the vectors h R and h P , when computed in a similar fashion as mentioned above were generally high (about 0.8) (see Table 6).


Since the script and sequence structure of English is very different from these low-resource languages, the addition of English to the limited target language training data yields a considerably noisy corpus.

we design a new neural architecture which integrates multi-level adversarial transfer into a Bi-LSTM-CRF to improve low-resource name tagging.


The second is based on multitask learning via a weight sharing encoder (Yang et al., 2016Lin et al., 2018).

to explore the impact of the size of target language annotations, we use 0, 10%, 50%, or 100% annotated training data from Amharic.


Since the extracted sentence pairs are only partial translations, incorporating them as they are into the training data for NMT may mislead the training of the model due to their noisiness.

all our NMT systems, including baselines, were the Transformer model (Vaswani et al., 2017) trained with Marian (Junczys-Dowmunt et al., 2018).


Even without an accurate translation model, we still have the possibility of extracting sentence pairs from unrelated source and target monolingual data.

with the harmonic mean F t between c s and c t , the algorithm searches for target sentences containing as many words translating source tokens as possible while penalizing the retrieval of very long target sentences that may also contain many tokens having no counterparts in the source sentence.


Furthermore, we also observed the complementarity of backtr and copy (backtr+copy), with 4.2 and 2.3 BLEU points of improvements for en→de and en→tr, re-spectively, over the baseline system.

marie and Fujita (2018) presented a method for inducing phrase tables from monolingual data using a weaklysupervised framework.


We speculate that the standard phrase table trained only on 18k sentence pairs is not strong enough to bias the extraction of partial translations.

most of them rely on the availability of document-level information, in comparable corpora for instance, and usually for one specific domain, to efficiently extract accurate sentence pairs (Abdul Rauf and Schwenk, 2011).


Our model also outperforms the supervised models in Ukrainian and Latvian.

our experiments on 68 treebanks (38 languages) in the Universal Dependencies corpus achieve a high accuracy for all languages.


3 To show the generality of different methods, we consider European, non-European and low-resource languages.

finally, we summarize our findings with possible future directions in Section 6.


Consider the task of translating for an extremely low-resource language pair.

the adversarial training tries to force the sentence representation generated by the encoder of similar sentences from different input languages to have similar representations.


We choose four language pairs for this purpose: Arabic↔French which are distant languages, and Ukrainian↔Russian which are similar.

one recent exception is Neubig and Hu (2018) who trained many-to-one models from 58 languages into English.


The resulting cross-lingual embeddings can be used to share supervision for lexical classification tasks across languages, when annotated data is not available in one language.

even for our development language German, results suggest that there is room for improvement.


We trained monolingual ELMo and FastText with default parameters.

for monolingual training, we used the 1 Billion Word benchmark (Chelba et al., 2014) for English, and equivalent subsets of ∼400 million tokens from WMT'13 (Bojar et al., 2013) news crawl data.


Intuitively, a sentence is less ambiguous than stand-alone words since the words are interpreted within a specific context, so a mapping learned at the sentence-level is likely to be less sensitive to individual word inconsistencies.

this is an interesting   observation and may indicate that contextualized dictionaries result in a more balanced mapping, while context-independent embeddings overfit the mapping to the specific direction used for alignment.


using such words in the alignment dictionary may result in suboptimal overall mapping.


These mixed results suggest that while crosslingual transfer in neural network models is a promising direction, the best blend of polyglot and language-specific elements may depend on the task and architecture.

we also draw a sharp distinction between multilingual and polyglot models.


When training a multilingual parser, it could be interesting to explicitly represent these parameters, and to integrate them into the parsing model.

we would like to use multilingual word embeddings to make lexical information accessible to the parser, making it more realistic.


our model extracts sentences from a given document and further compresses these sentences by deleting words.


Our model selects a set of sentences from the input document, and compresses them by removing unnecessary words, while keeping the summaries informative, concise and grammatical.

abstractive systems require natural language generation and semantic representation, problems that are inherently harder to solve than just extracting sentences from the original document.


An increase in the proportion of uncommon words makes the models also generate uncommon words, which are not likely to match the ground-truth, thereby reducing the recall.

evaluation Metrics: Studies on textsummarization evaluate their system using Rouge; therefore, we report Rouge-1 (unigram), Rouge-2 (bigram), and Rouge-L (longest-common substring) as the quantitative evaluation of models on our corpus.


We then sample 255k instances that have all associated short texts in them.

click-bait, a ShortcutText, contains mostly elements that create anticipation, thereby making a reader click on the link; however, the reader comes to regret their decision when the story does not match the clickbait's impression (Blom and Hansen, 2015).


For example, as shown in Figure 1 and assuming that stemming is performed, the verse Isaiah 25:8 would be properly linked to 1 Corinthians 15:54 due to the two verses sharing the terms 'death' and 'swallow'.

but with such a father and mother, and such low connections, I am afraid there is no chance of it."


We also examine what our model got wrong in proposing Bible cross-references.

the Gram-Schmidt based anchors produce better true positive rates for very low false positive rates.


In general, both summarization and chat generation tasks often use automatic evaluation metrics to evaluate generated sentences, their scores tend to be much lower in the chat generation task.

i heard that major beer companies are planning to increase beer production by about 10% this summer compared to last summer.


Such situations are growing more popular in recent years with the rise of voice-controlled conversation systems such as intelligent assistants (e.g., Siri, Alexa, and Cortana) (Jiang et al., 2015;Sano et al., 2016;Akasaki and Kaji, 2017) and smart speakers (e.g., Amazon Echo and Google Home).

on a suicide bombing that happened at a concert in England, the homeless action around Separate (Gen+MMI): I heard that the homeless action around the scene of a suicide bombing that happened in England was praised.


(2016) proposed two learning based methods for an LSTM encoder-decoder: LenEmb and LenInit.

for English, the proposed method also generated headlines with the desired length precisely and achieved the top ROUGE scores on the DUC-2004 test set.


This test set is suitable for simulating the real process of news production because it is constructed by a Japanese media company.

these parts show that the proposed method achieved comparable scores to ones trained on whole training dataset.


The sentence-level encoder GRU iteratively takes the embeddings of the words in a sentence to update its hidden state, thus its final hidden state is a representation of the sentence.

the decoder GRU takes the current dialogue representation to initialize its hidden state so as to generate a response sentence word by word.


Besides, Table 1 also shows that appropriately scaling up the model brings better performance but consumes more resource, which implies that the simplified HRED will perform better than the baseline HRED when time or memory is limited.

hierarchical Recurrent Encoder-Decoder (hRED) is a conversational model for building end-to-end dialogue systems.


In this work, we focus on positive explanations.

to avoid having too many negative examples in our modified datasets, we only consider sentences that contain at least one candidate.


span-based training data is more powerful.


We show that a multiple-choice version of Hot-potQA is vulnerable to the same baseline that performs well on WikiHop, showing that this distinction may be important from an evaluation standpoint.

looking at the no-context baseline for comparison, we find that it is only around 2% lower than the two relatively more complex models.


Our combination strategy yields larger gains over the SMT baselines than simpler rescoring or pipelining used in prior work on hybrid systems (Grundkiewicz and Junczys-Dowmunt, 2018).

both SMT and neural sequence-to-sequence models require large amounts of annotated data.


Moreover, the calculation of output o h are restricted to the a single individual subspace, overlooking the richness of contexts and the dependencies among groups of features, which have proven beneficial to the feature learning (Ngiam et al., 2011;Wu and He, 2018).

cNNs, revealing that extracting local features with dynamic weights is superior to assigning fixed parameters.


We fail to replicate the reported results of SGM on AAPD using the authors' codebase and data splits.

(2016) and we are able to achieve good classification results without attention mechanisms.


We also provide the standard deviation of the scores across different seeds to demonstrate the stability of our results.

both of the models surpass the original result by nearly two points for the IMDB dataset.


The highest coverage ratio for each attribute is usually obtained when masking that attribute in the distractor MR (entries on the main diagonal, underlined), in particular for FAMILYFRIENDLY (FF), FOOD, PRICERANGE (PR), and AREA.

the base speaker S 0 model is often underinformative, e.g., for the E2E task failing to mention certain attributes of a MR, even though almost all the training examples incorporate all of them.


a person in a green jacket it surfing while holding on to a line.

our KL-penalty further improves the stochasticity of WAE, as we achieve the highest performance in all diversity measures.


We investigate the utility of sequence-to-sequence models with attention (Bahdanau et al., 2015) to generate concrete realizations of abstract task descriptions.

add a cut amount of cucumber ... Gold put the wrap on a plate .


In contrast, qualitative analysis of the unparameterized transition model show that its alignments learn desirable correspondences (see Figure 2).

the forward variable α i (j) representing p(y 1:j , a j = i|x 1:i ) is recursively as αi(j) = p(yj|i, x1:i, y1:j−1) × i k=1 α k (j − 1)p(aj = i|k, x 1:k , y1:j−1).


We expect all the models to implicitly capture aspects of world knowledge.

most relevant to this paper is the pioneering work of Regneri et al.


One minor exception is that neither predictability nor frequency improves significantly over the other in Dundee.

one major such confound is temporal diffusion (i.e.


fixation duration) (Demberg and Keller, 2008;Frank and Bod, 2011;van Schijndel and Schuler, 2015;Shain et al., 2016).


Because only a few classifiers are suitable to modify a given noun (again, see Table 2) and the entropy of classifiers for a given noun is predicted to be close to zero, MI between classifiers and nouns is expected to be high.

we also assume that every classifier-noun or classifier-adjective pairing we extract is equally acceptable to native speakers.


For example, most nouns referring to animals, such as 驴 (lǘ u, donkey) or 羊 (yáng, goat), use the classifier 只 (zhī).

(2010, 216) that show that children as young as three know classifiers often delineate categories of objects with similar shapes.


Although it does not make it into the top three, MI for the SPATIAL sense is still significant.

it's possible that native speakers differ in either their knowledge of classifier-noun distributions or confidence in particular combinations.


In fact, in standard English, inside sentences, only proper nouns start with upper-cased letter thus fine-tuning the pre-trained model fails to slough this pattern which is not always respected in Tweets.

to do so, we follow (tamaazousti et al., 2017) and analyse the units of Φ (biLStM layer) before and after finetuning.


Furthermore, we have observed that despite the normalisation, the performances of the pre-trained classifiers were still much better than the randomly initialised ones.

indeed, as illustrated in the left plot of Fig.2, at the end of training, the distribution of the random units' weights is still absorbed (closer to zero) by that of the pre-trained ones.


via maximum likelihood estimation), is the weight used for redistributing the preserved mass (e.g.

in neural LMs this remains an open question (Kawakami et al., 2017;Kim et al., 2016;Cotterell et al., 2018), while a common practice is pruning the training corpus and imposing closed vocabulary assumption (Mikolov et al., 2010) where rare words at training and unseen words at test are treated as an UNK token.


Our data augmentation method uses UM inflection tables and creates additional training examples by finding Wikipedia sentences that use the inflected wordforms in context, pairing them with their lemma as shown in the inflection table.

finally, we shuffle the extracted sentences to encourage homogeneous type distribution across the entire text.


The representation of a local l-gram window is obtained by an improved approach over Eq.

this illustrates the effectiveness of our proposed model from a general perspective.


Nielsen and Chuang (2010) introduced three measures namely trace distance, fidelity, and VN-divergence.

the corresponding trainable components in state-of-art neural network architectures, namely, kernels in CNN and cells in RNN, are represented as arbitrary realvalued without any constraints, lead to difficulty to be understood.


Experiments on benchmarking QA datasets show that CNM has comparable performance to strong CNN and RNN baselines, whilst admitting post-hoc interpretations to human-understandable language.

it is computationally costly to compute these metrics and propagate the loss in an end-to-end training framework.


In total, each formulated question is accompanied with five candidate answers, including one # CONCEPTNET distinct question nodes  2,254  # CONCEPTNET distinct answer nodes  12,094  # CONCEPTNET distinct nodes  12,107  # CONCEPTNET distinct relation lables  22  average question length (tokens) 13.41 long questions (more than 20 tokens) 10.3% average answer length (tokens) 1.5 # answers with more than 1 token 44% # of distinct words in questions 14,754 # of distinct words in answers 4,911 Verifying questions quality We train a disjoint group of workers to verify the generated questions.

in total, five candidate answers accompany each question.


Our results show that under limited textual context, models are capable of leveraging the visual input to generate better translations.

we analysed the behavior of state-of-the-art MMT models under several degradation schemes in the Multi30K dataset, in order to reveal and understand the impact of textual predominance.


2017, which promotes fairness by requiring that the covariance between a protected attribute and a data point's distance from a classifier's decision boundary is smaller than some constant.

similarly, the TPR gender gap for occupation c is TPR g,c = P Ŷ = c | G = g, Y = c (10) where g and ∼g are binary genders and G is a random variable representing an individual's gender.


Using λ = 0 leads to significant gender bias: the maximum TPR gender gap is 0.303.

given a sufficiently large number of clusters, CluCL is able to simultaneously mitigate multiple biases, including those that relate to group intersections.


We begin to see some interesting trends in [16], who found that the use of the stereotypical sentence-final particle wa was positively valued when used by young women when attempting to convey a specific impression of femininity -'coquettishness'.

instead they were inclined to use sentence-final particles such as ne() and sa(..) either as a method of seeking support, and/or as a speech tactic to hold the floor in the conversation.


[9] gives an in-depth look at the Scottish English of Morningside, and looks into age variation according to sex.

for the same reasons financial demands rise.


An overall summary grade, representing facility in spoken English, is calculated as a weighted combination of the continuous measures and the categorical measures.

both the human scores and the machine scores for the Overall "facility" construct exhibit a reliability of 0.94, but we need to know if the machine scores actually match the human listener scores.


• T is a finite set of lexical items (hereafter, called token) composing the discourse segments.

in our approach, the frequency of co-occurrences for each pair of lexical items is collected and the collocation measure between them is calculated.


The SVD of the matrix W is then defined as the product of three matrices, where the columns of B contains the eigenvectors of W and / is a diagonal matrix containing the eigenvalues in descending order: The eigenvectors are normalized to have length 1 and orthogonal, which means that they satisfy the following condition: B TB= I. Decomposing a regular matrix into a product of three other matrices is not too interesting.

the truncated SVD matrix is used to show the high coherence relationship of the segments in the document, and to estimate the structure in segments across the document.


Since each lexical item must be declared in the Cooking Lexicon, it offers an overview of lexemes used in the experiment.

this resultative meaning vitally affects acceptability of the sentence.


The abstract case and tense can be spared, and the notion of trace is coped with in terms of structure sharing between NPs.

chafe (1970:  chafe (1970: 132) also introduces a variety of derivational rules such as causative, inchoative, and resultative, etc.


Although ba is a resultative verb, the resultative meaning does not reside in the lexical content.

the cooking domain involved here is very small, but functionable.


With all this information properly encoded in the Cooking Knowledge, the parser is capable of distinguishing abnormal utterances like 'cut the oil' from logical sentences in the cooking domain.

in the matrix diagrams of ha, the resultative verb is lexically specified as `non-resultative' in SEMTYPE.


A sentence like Sono Eikokujin wa wakaranakatta (That Englishman wa didn't understand) is better that Sono Eikokujin ga wakaranakatta.

compare Sachi wa nihon ye kaerimashita (Sachi wa to Japan returned) with Sachi ga nihon ye kaerimashita which in some cases (more cases than with `wa') suggests that Sachi is unique in her returning, or at least that Sachi makes up a complete list of (relevant) people who have returned.


• It follows that there is an object u in w which satisfies `piza( t)' in every world wherè watashi' is relevant.

this means that `watashi wa piza' is a better answer, for `piza' may be singular thus making `watashi ga piza' peculiar.


win-KA lose-KA-TOP time-ANP chance-COP-PRES "Victory or defeat depends on chance."

in Japanese, conjunctive coordination is expressed with the conjunctive particle "mo" while disjunctive coordination is expressed with the disjunctive particle "ka", as shown in (1):3 (1) a. Ken-mo Naomi-mo ki-ta.


In cases where the whole set of passengers passed through either the west gate or the east gate, this sentence would not be felicitous.'

ken-MO Naomi-MO come-PAST "Both ken and Naomi came."


Another partitioning use of "ka" is found in embedded questions as shown in ( Here are three variations of embedded questions in which we can see partitioning into two propositions.

in a non-indicative statement such as a generic statement, we find determinacy of the truths of related propositions and disjunction gets "and"-reading.


In this case, the lack of clear-cut contrasts does not affect the legitimacy of a defining relation.

we appreciate very much colleagues at our corpus linguistics and lexical semantics working group for their input and discussion.


chat can be measured in terms of its temporal duration.

the first is that diathesis alternations have not been extensively studied in Mandarin, unlike English, where as Levin notes, there were several important studies done on the verbs cut, hit, break and touch prior to her own work.


In example (14a), the focus is on incremental theme and therefore the measure phrase describes the resulting number of wounds.

since they do have totally different (logical) event structures, previous theories may have to treat them as homophones.


Our goal is to locate the linguistic relation that defines the contrast.

the first assumption is that lexical semantic contents are mapped to the morphosyntactic level and can be used to predict grammatical behaviors [e.g.


Ker's work clews us the feasibility of pure linguistic approach in the resol utio of the alignment problem.

one of the best examples about semantic similarity between two languages is bilingual dictionary , almost all of the source word s have their translation in target language.


(18) shows a possible derivation of the CP ai-te ar hi (17).

it is not argument-sharing but functional control that brings about thisi effect.


In Nikkei's case, the error rate for adapted lexicons is generally higher and its improvement by adaptation is not as obvious as Kyodo.

in fact, it makes the best total performance.


In our current experiments, we only sum up the word counts of correspondent vocabulary to have a new ranking, which represents the exploitation in both resources.

they are therefore chosen as our main targets and the following OOV statistics are basically referred to them.


The segmentation error rate (= (substitution + insertion + deletion) / total words) is evaluated by comparing the reference (manually segmented) data with the automatic segmentation result, which is done by a statistical approach with a lexicon involved.

we don't need them in target lexicon because they can be treated as a sequence of Arabic digits or Kanji number segments 9, depending on how they are pronounced in speech recognition.


for (2a), for instance, the predicate ahure 'to overflow' and the predicate more 'to leak' specify that the subject argument is some form of liquid, and the argument yokusoò bathtub' specifies further that it is water.


Thus, this kind of data lends very strong support to the empty-category analysis or the analyses which turn to pragmatics exclusively to decide the semantic .

one of such behaviors concerns the identification of the antecedent (henceforth, the (semantic) target) of the IHRC.


The targets in (2b-c) are not allowed to occur on the surface: ( If the target does not surface, there seems no straightforward way to syntactically accommodate the structure.

in Japanese, the expected reading is available only in the form of the iHRC.


Meanwhile the two nominative NPs in the psych pattern are in a regular stative transitive construction.

in addition to providing an accurate reflection of the sentence meaning, it predicts the behavior of the first NP.


b. FP: Sensayngnimi -i soncai-ka cakiii*j pan-eyse ceyil yeypputa.

the sentence does not say of John that he is good in general (the interpretation that would be required if coh-ta combined directly with John).


For example, the tail, chaek-ul 'book-acc' in (3c) is moved to the left of the contrastive focus, Nuku-ka 'who', and constructs the presupposition, 'x threw the book' along with the verb tenchi-ess-ta 'threw'.

in (8B2), the old argument, Suni, is deleted without any informational function.


The most transitive sentence in this respect is one in past tense, perfective aspect and indicative mood (for example 'you have built the house').

the definition I have proposed in this paper does not cover all the possible phenomena and does not always enable us to choose only one sentence (type) to present the prototypical transitive sentence of the language in question.


The antecedents of the null anaphors in the above dialogue are related to the hotel reservation.

(28) Ul: kulayse incey ey cohci nayka kulay hanta hay kaciko hay therefore well oh good me so try determined Pollanikka talun kenun casinissnuntey swuhak-i mwunceyeyyo.


Sets are incorporated into the rules and categories of CCG in order to allow flexibility for handling scrambled word orders directly with single lexical categories.

set-CCG also accepts scrambled orders with single lexical categories by incorporating sets into the categories and rules of CCG.


As a result, representation comes to play an important role of bridging the and computational processing.

although they are pure conceptual constructs, tables are meta-theoretic objects to which mathematically explicit operations can apply.


Suppose we have a simple sentence: (15) Mia loves Kim.

representation comes to play an important role of bridging the gap between human and computer interactions.


In the same time they exhibit several important differences with respect to the syntax and semantics of the quantification in natural languages.

de re interpretation in which there is a specific movie and all students in the described situation are watching it.


If we assume that THC > LPC > CCC (brief form for our convenience) in constraint hierarchy is correct in Korean, one can obtain the right anaphoric interpretation in psych construction as illustrated in tableu 31 In (32) John satisfies THC but violates CCC because it stands structurally lower than 'himself.'

even though LOC seems to be a weak-motivated constraint, I assume that it exists universally in both languages.


It has been argued in the literature, however, that the dative argument occupies a higher position than other arguments.

(29a) with the nominative possessor is derived as in (30) In kono inu-no atama, the category TP/LTPpos is assigned to atama 'head' because it is not an independent entity but a part of it (a function taking possessors to return their heads).


While there are expressions which were strongly associated with either female (e.g.

for example, having four legs, tails, beaks, wings, and so forth are some salient properties of animals, but they are not mapped onto the concept of woman.


Putting this process into a general model, we may get the following (see 4.4 for further discussion): The property of the target concept is defined socially.

according to their view, "human thought processes are largely metaphorical" and the "human conceptual system is metaphorically structured and defined" (6).


According to the conceptualization in Japanese, human male is considered a work force, whereas human female is considered untrained.

does not mean that human beings require the same kind of energy as machines.


while object topicalization is quite marked and appears primarily in spoken register in English (Lambrecht [8]), it is not so marked in Japanese and is allowed more freely in written register.


(6) and (lb) differ with regard to the information status of the logical object.


How, then, can cases of symmetric projection be distinguished from those of asymmetric projection?

then, the lexical category go, and the determiner the, which is a functional category, are characterized as follows: (21) a. go: =0 (goal), =0' (agent) b. the : =n One of the distinctions that are standardly assumed between lexical and functional categories can be captured in terms of these two types of selectional features.


TOP until waited but after all call TOP not existed 'John waited until (the train got to) Osaka, but after all he didn't have a call.'

moreover, the semantic feature [unspecific], which originates in ka and is crucial for the interpretation of the second phrase, projects up to both phrases.


Licensees and licensors are features involved in movement.

as for the first question, Nishigauchi in [7] makes an important observation on pairs of examples such as (14a,b): (14) a. Dare ka kara henna tegami-ga todoita.


Relations between positions as preposed (initial) and postposed and discourse functions of temporal, conditional, and causal adverbials have been done quite a few.

(12) in English is a counter-factual sentence (though there's no syntactic representation of counter-factual in Chinese, it is obtained through discourse).


In section 2, we see how the connectors under temporal and conditional clauses are distributed in Chinese discourse.

consider the notion of time flow: it is more natural if we mention A first then B by sequence; the inversion of time sequence is not so natural but serves the function of topic continuity.


Temporal clauses deal with time, and conditional clauses deal with hypotheticality.

the distribution is that temporal and conditional clauses tend to occur before their modified material (preposed/ initial), while causal clauses are more likely to occur after the modifiee (postposed/final).


An alternative question presents two or more options for the reply.

in this paper, we deal with English alternative questions such as (1) within the framework of Head-Driven Phrase Structure Grammar.


Conversely, if one of the clauses is non-ta-marked and the other is a to-form, the interpretation from the utterance time perspective entails that from the perspective of the matrix clause time.

in these foregoing studies, a feature MEDiUM-TiME was introduced as a mediator of temporal information between the subordinate and matrix clauses, which is only motivated by the difference in the tense interpretation of non-ta forms depending on their Aktionsarten.


Traditional studies on tenses in complex sentences have referred to the tense marking in the subordinate clause as 'relative tense,' distinguishing it from the 'absolute tense' indicated by the matrix predicate (see for example Terarnura 1984).

although in (20) the embedded clause nai-te iru (is crying) is by default interpreted in relation to the tense of the matrix predicate, it can be extended with adverbials such as ima (now) and asoko de (over there) that make a reading more reasonable in which the embedded tense is interpreted in relation to the utterance time.


This in fact keeps in line with the observations made so far, as long as the relative clause is involved.

they are not an exhaustive study of tenses in Japanese subordinate clauses: the subordinate clauses taken up there are limited to relative clauses, the quotative clause marked by to and to yu, the temporal clause introduced by toki (when), mae (before), and ato (after), and nominalization by no and Moto.


Keenan Stavi 1986, Keenan 1996) considers that they result from the application of a discontinous determiner to a common noun.

examples are given in (29) and (30): (29a) every student will swim unless it is raining (29b) every student will swim except if it is raining (30a) Leo will go to the party unless he is tired (30b) Leo will go to the party except if he is tired Now it is clear that given the above equivalent sentences of the first type can be directly analysed in the same way as the sentences in which eXCL phrases occur and which are analysed in the preceeding section.


Now we can also build up a rough semantic generalization of Table-1 of Korean PSIs.

b. Affective polarity items are licensed by nonveridical operators/connectives.


In contrast, amwu-na reads as 'anybody, irrespective of the quality he has'.

(20) a. Jini-malko tto nwuka hapkyekha-ess-ni?


This allows us to link the names of frames, frame elements, and other items in the database directly to the annotation tags.

in a sentence like The bartender asked for my iD, it is the individual who occupies that role that we understand as making the request, and the request for identification is to be understood against the background of that frame.


But here the task of lexicography is more restricted: to characterize the combinatorial requirements and possibilities that are specifically associated with individual words rather than with the possibilities provided by the whole grammar of the language.

the word widow evokes a complex historical frame in which a woman is seen as having a particular social status because the man to whom she was (most recently) married has died.


Moreover, the distributional differences that exist for Mandarin verbs of putting bai3 and fang4 (such as co-occurence with a progressive to describe a process (2a), taking a resultant object (3a), and being modified with an orientational adjunct (4a)) do not exist in English (see examples 2b-4b respectively).

in all the cases in (11) below, the locative role is a metaphorical extension to an abstract concept such as 'quandary' or 'trial.'


conceptualizations of 'set' and 'put' in English and Mandarin have different semantic and syntactic entailments.


The results of the last experiment provided results for "conceptual clusters".

when a cluster surfaces based on conceptual categories but not collocation statistics, inaccurate translations result.


The specific CA technique used in this study is an agglomerative hierarchical method.

for languages with little morphology, a different approach is necessary.


In this case, the implicit argument of else acts like a bound variable and what is obviated varies according to the quantifier to which it is bound: (10) a.

we should seek some other view on the exceptions.


(26a) vs. (26b) below:7 Compl X Compl X (a) Specifier on the left (b) Specifier on the right It turns out there is a considerable difference both in terms of the number of LR actions performed and the stack depth required to process an example like formalize, analyzed as formal-i(z)-e in (27) below:  7Two other specifier-head-complement configurations can be obtained.

we define the local domain of an asymmetrical relation r as the smallest domain where r applies.


The contribution of particular elements of a theory to computational complexity can be determined through experimentation.

to limit this for inc and caus, we appeal to semantic considerations: that is, inc and caus occur at most once per singular event.


We also try to place emphatic particles in their proper place within overall grammar of the Japanese language.

(2) commits the speaker only to the possibility of somebody other than Taro also having come, whereas (3) to its truth.


We specifically looked at sika and claimed that it is different from other Japanese NPIs.

14Taro-sika ookina-kemusi-no iru ringo-o tabe-nai Taro-SIKA big-worm-be apple-OBJ eat-NEG Only Taro eats an apple which a big worm is in' (15) Taro-sika kasikoi-tori-kara kakureta ookina-kemusi-no iru ringo-o tabe-nai Taro-SIKA wise-bird-from hiding big-worm-be apple-OBJ eat-NEG Only Taro eats an apple which a big worm that is hiding from a wise bird is in' sika construction can embed another sika construction, as can be seen in the following: (16) Taro-sikal Ken-ga eigo-sika2 ie-de hanas-anai2 koto-o sir-anail Taro-SIKA Ken-SUBJ English-SIKA house-at speak-NEG fact-OBJ know-NEG Only Taro knows that Ken speaks only English at home' Taro-sika is interpreted as associated with sir-anai, hence the same superscript, while the internally embedded eigo-sika is interpreted as associated with hanas-anai.


And we derive the topics ranked by weights as follows: Egress, Focus, Posterity, Disease, Completion.

many references have shown that information in machine-readable dictionary (mRD) is an unbiased knowledge source for WSD (Guthrie et al.


They reported conceptual representation acquired from MRD for a word sense did improve the precision of sense tagging on ambiguous words when compared with the corpus-based approach.

although it has good coverage (Farreres et a1.1998, Kwong 1998) and its synset is much like a thesaurus, the synset fails to provide an explicit classification.


We sum up the above descriptions and outline the identification of relevant conceptual list algorithm as follows.

it would also be difficult to generate appropriate representation of conceptual categories for the WordNet senses.


The integrated resource is readily usable, and we have discussed some potential use of it in WSD.

wilks, 1975;Small & Rieger, 1982;Miller et al., 1990).


However, though we have these rules of thumb for our judgement, with borderline cases we have but our intuition to count on; and since it is quite difficult to distinguish such varying degrees of accuracy in practice, we will group them into a single category called Accurately Mapped (A).

hence we can exploit the structural inter-relatedness to map senses from the dictionary to the mediator and from the mediator to the thesaurus separately.


This was because we observed that high level nodes were often too general to be helpful.


Although in the current study we have focussed on the direct mapping from the mediator to the thesaurus, the evaluation criteria described below also apply to the direct mapping from the dictionary to the mediator, and most of them to the indirect mapping from the dictionary to the thesaurus.

the LDOCE definition corresponding to the sense of synset X (i.e.-Dx) is expected to be similar to the textual gloss of synset X (i.e.


y is a position indicator, range from 0 to 15.


If the first reading in (10c) is chosen, the transition state in (10d) is CONTINUE, and if the second reading is chosen, it is ROUGH-SHIFT.

under our analysis, (5) can be represented as in (11).


it precedes the utterance time, because the past tense is used.


Nevertheless, they need further elaborations.

they may possibly signal turn-initial positions or they may express special pragmatic functions such as surprise or hesitation.


Words used to initiate turns mark the beginning position of all turns and at the same time they are also beginnings of a number of utterances.

it is very likely that discourse particles and speech disfluency are highlighted by prosodic means to emphasise the semantic and syntactic inadequacy.


The extra-linguistic knowledge that the speaker is male may be indicated by describing the fact with some conjunction like where: e. where male( ) When the extra item is added as in (5), the effect is usually taken into account by restricting the domain where the semantic representation should be interpreted.

per son(i) When combined with the set of axioms (4), the axiom turns the give semantic representation into the ohter below: aru I (vehicle(x) 3.4 Points to note We ensure that the exponential, !, should not distribute under any other connectives but the multiplicative one.


c. He was excited that he could finally buy a piano.


the focus of this phonetic experiment is on the middle stray mora, which may possibly attach either to the preceding foot (F3), or the following foot (F4).


In HAPPINESS IS PLANT metaphor, HAPPINESS maps to the result of cultivating a plant.

growth is an image-schema in the source domain of plant that is preserved when mapped onto the target domain of love.


Focusing some element in a sentence amounts to making a statement to the effect that some predication holds of the object that corresponds to the focused element in some other circumstances.

in (15a) is somewhat strange in isolation without "Tabe-naku-te-mo, hutoru."


Contrast these with wa and mo attached to nominal phrases.

the presupposed number must be less than three.


Like Cho & Chai (2000), our analysis also provides the same explanation for various scrambled sentences such as the Double Accusative Construction (DAC) in Korean.'

this difference in the scrambling possibility just follows from the fact that under our analysis, (18b) violates the RM and ANI Constraint of the Argument Constraint but (19b) does not violate any LP Constraint.


(The PM value of the NP Mary is `Acc/Daf , while that of the NP ton is 'Ace) Nor does the RM ANIMACY Constraint apply to this case, since their ANIMACY is different.

when the location is focused, the adjunct NP i san can bear a (contrastive) 'focus' marker, which happens to have the same morphological form of nominative case, as shown in (9b).


All features of functional categories are typically uninterpretable.

although I do not discuss the structure of abstract nouns, I assume that the analysis ensued for non-abstract nouns will carry over to them as well.


However, if the noun refers to a specific object, a classifier must be present.

further, under K, both Num and CL must be lexically realized.


(30) a. Tom has written a novel, but Peter never has i. b.

4 Following Zwicky and Pullum (1983), we take n't to be an inflectional suffix.


This prediction appears to be correct: It is an important semantic fact that a VP modifier never outscopes a higher verb.

based on these empirical properties of auxiliaries, this paper argues for the existence of the construction aux-headph(rase) whose subtypes include negation-ph, inversion-ph, ellipsis-ph, vp-filler-ph, and the like.


That these languages do not show Complex NP and Adjunct island effects is well-known and it has been proposed that it can be explained if what is moved is the the whole island containing the WH-phrase, not the WH-phrase inside the island (Choe 1987;Nichigauchi 1986Nichigauchi , 1992Kishimoto 1992;Yoon 1999, etc.).

wH-in-situ languages like Korean, Japanese and Sinhala could be taken as languages where both the direct and indirect wH-feature checking movement are covert.


However, if Based on this contrast, he argues that pied-piping is a last resort measure made possible only when it is forced by the need to ensure convergence: pied-piping the DP in (i) is grammatical since moving the WH-word alone will violate the Subject island constraint The same does not hold in (ii) since moving the WH-word out of the object DP is possible.

imbabura Quechua (9) [ima-ta Juan randi-slikato-taj pro ya-ngui?


Given that the indirect WH-feature checking movement is covert in English, the prediction is that an XP can be pied-piped even when the WH-phrase is not in its Spec overtly as long as it could move into the Spec covertly.

this will be crucial in my account of pied-piping in English, as will be seen shortly.


What is crucial in our discussion on the existence of covert pied-piping is that in order for an element in syntactic islands like complex NPs and adjunct clauses to be questioned in Sinhala, the second part of the WH-word, i.e., d a , must appear at the edge of the island, not next to the first part of WH-word inside the island.

in my analysis clausal pied-piping in these languages is explained in the following way: On its way to the Spec of +WH-C, the WH-phrase will pass through the Spec of intermediate CPs due to Subjacency.


Given the hierarchy (12), the type nonfinite-head-subject-construction is a subtype of the type clause and also a subtype of the type head-subject-phrase, and so it inherits all the properties of the two supertypes.

it has been long recognised that verbs like begin are ambiguous: one belongs to the strict transitive type taking a pure NP complement while the other is what we might call an oblique verb as it takes an infinitival or participle VP complement obligatorily.


The Subcategorization Feature Principle in Fig.

they are represented by the lack of constraints on SUBCAt feature and the non-empty value of tENSE feature in ADJCNt feature, respectively.


Adjunct-head schema (b) corresponds to Head-Adjunct.

pseudo-lexical rule schema allows the higher constituent of the complex predicate to inherit the argument structure of the lower predicate, and as a result, the schema makes the two predicates form a constituent in syntax.


To reflect the transition, IHRC must rather be defined in relation to the main verb.

it is characterized by the apparent syntacticsemantic discrepancy; iHRC apparently involves a clausal complement, which, however, designates not the event denoted by the clause but a certain entity participating in the event.


Note that this phenomenon is separate from the development into adverbial clauses which took place 1000 years ago.

the indirect linkage via R-relation is an advantage in that it offers a more comprehensive and adequate account of IHRC.


The plausibility of this kind of approach can be testified in the other well-studied cases as shown in 19).

many previous studies on gerund phrases either do not defend endocentricity, or create a new syntactic category, or restrict the analysis to some subtypes of GPs.


I like (it) very much for him to apply for the position.

verbal gerunds are still a kind of verbs and their forms are determined by the usual grammatical apparatus.


3 Syntactic Analysis We use the CKY algorithm that was developed for the Chomsky normal form to parse a sentence.

we use only 1,000 sentences to find the optimal segmentation words.


In addition, besides the part of speech information they used word information.

the probabilistic model is composed of two probabilities, the phrase occurrence probability and the modification probability.


Therefor we constrain the position of the indices by reference to the keywords.

these are conditional probabilities on the semantic role and part-of-speech given a set of possibly contexts.


The parser consists of two main modules, a training module and a parsing module.

the current standard evaluation approach which consist in testing on unseen 10% of a training corpus may serve neither of these Figure 8: The closure aims, as it does not distinguish the system quality from the representativeness of the training data for the testing data.


Following Culicover andRochemont (1983, 1990) and Horvath (1986), we in this paper assume that this essentially semantic conception of 'Focus' can be characterized as a purely formal syntactic feature [+Focus] or [+F], which gets assigned to constituents at a certain appropriate level of syntactic representation, triggering such syntactic operations such as 'Movement' and 'Adjoining' under the general syntactic principles and constraints.

this generalization holds true only in Mandarin and perhaps in some other varieties of the Chinese language but certainly not in all varieties of the language.


From the constraint (16), the Focusing Rule and the Focus Resolution Rule are forced to apply somewhere inside the clause.

(In this case, the whole NP with the sentential modifier is focused and associated with mo.)


The meaning of this NP is specified in the CONT feature.

in (7b), the associate can either be Taroo or imooto or Taroo no imooto.


In English, association with focus is more or less a clear-cut phenomenon.

in order to do so, we need the definition of the function only.


But this is not the case; (6a) simply does not have such reading.

i TOP Hanako DAT Taroo ACC introduce do PAST only COP "i introduced only Taro to Hanako."


in the case of NFA), this rule checks if the FOC values of the head daughter and the FP have identical values with respect to the TG and OP features.

at least according to the intuitions of mine and some native speakers I consulted, the sentence (i) does not seem to have such drastic contrast in interpretation to its counterpart in which the case marker o is missing (i.e.


bread only ACC eat PAST (I) did nothing but eat bread.

the sentence would be assigned an illicit interpretation.


Taroo is said to be included in the event if it is coreferential with the argument of the verb.

b. Hanako-ga tonari-no gakusei-ni piano-o asa-made hik-are-ta.


assassin-Nom senator-Acc kill-past "The assassin killed the senator."

the adversative interpretation does not arise.


Moreover, the V in this construction must be a transitive verb whose subject is an agent.

we have identified a potential lacuna in the organization of a clause in terms of pertinent arguments.


As shown by (19a), a noun phrase in a genitive possessor induces an i-within-i violation if it is coindexed with the entire possessed noun phrase.


The words listed as entries in an Indonesian dictionary are usually root words; the various affixes applicable to a root word are usually listed together in the corresponding entry.

this situation is inevitable since we have to handle morphing and thus we have to consider every possible replacement of the first letter(s) of the stem.


How to avoid or reduce the SVD cost is the major problem of LSI approach, especially for multi-linguistic IR.

how to represent a document on a semantic basis rather than on a pure statistical basis is one of the chief goals of our further research.


Kim 1998a The key point of the KPSG treatment includes the type constraints on v-mod-w and gapintroducing rules.

for a verb stem to appear in syntax, it should be inflected at least with a mood marker (cf.


The output of this stage becomes the immediate input to the syntactic analysis component, bypassing the tokenizing and morphology transductions that perform these functions for the other ParGram languages.

f-structures are meant to encode a language universal level of analysis, allowing for cross-linguistic parallelism at this level of abstraction.


Topicalized noun phrases may have certain postpositions before the final ha, as in (3a).

just as with the japanese ha, the Korean topicalizer also cannot cooccur with postpositions marking the basic grammatical functions, as in (3d).


We anticipate no problem for open class items with predictable subcategorization frames such as nouns, adjectives, and adverbs.

the Japanese entry for wo was ported to the Korean entry for +Acc.


In this regard, Jackendoff is absolutely right, when he suggests that the mental rehearsal of sentences may be the primary means by which our own thoughts are able to become objects of further attention and reflection.

*I wish to thank Prof. Shuanfan Huang and two anonymous reviewers for their useful comments and suggestions.


The proofreader can see the other examples as references to determine whether or not each tagging result is right.

for instance, in Table 1, the keyword-in-context file of the word '1111: f9: (research), which has pos-categories of verb type VE and noun type Nv, is sorted according to its left/right context.


A cost-effective algorithm may not be the algorithm with the highest precision.

a better accuracy can be achieved if the tagging results are manually proofread.


U (wei) 'hello' with a rising tone illustrates a greeting, particularly used to initiate a telephone conversation.

this word will bring about effects on the audience, i.e.


It is shown that there is a special group of one-word small onomatepoeic speech act verbs which perform perlocutionary forces.

a Chinese character) for interjections and onomatopoeic speech act verbs in Mandarin Chinese tend to have the radical D mouth'.


Since we describe the onomatopoeic speech act verb as a syntactically independent monomorphemic utterance which performs perlocutionary forces, it is feasible to say that onomatopoeic speech act verbs are a subset of interjections.

examples are ill (pei)'bah' and I"-(heng)'humph'.


It is found two word forms for this particle in the Sinica Corpus IS and l ' -; .

they may appear as typical verbs as well.


A: "(No, that was because) we were going to buy a mat for my father; he slipped and fell down that morning."

(Su 1998)4 In this regard, the linkingfiu acts more like an utterance-connective, even when it does not combine with such connectives as ranhou, hou-lai and shuo-yi.


Results showed considerable variation and indicated that the phenomenon was relatively most common in Beijing text -up to more than 18% of verbs undergo the verb-noun transitional process to various extents.

in the last section we have seen that there is a continuum of the verb-noun shift and Table 10 shows examples with different r values.


(Linebarger 1987:338) For example, NPI is grammatical in the next sentence (13), for the reason that negation does not have scope over the because-clause in LF: (11) He didn't budge an inch, because he was just a wooden statue.

(Zwarts 1993;van der Wouden 1997) Zwarts (1993,1998) classifies three types of NPIs which are "weak", "strong", and "superstrong", according to DE-ness, anti-additivity and anti-morphy respectively.


for each category we learned a binary classifier and evaluated its performance on test set by computing a value of f1 .


Our results showed that the SVM and the variants achieved similar performance on Chinese with those on English for document categorisation for large categories, despite big difference between English and Chinese.

a document categorisation problem with only a few relevant documents for training may have similar situation with that in the right graph of Figure 1.


Regarding that some extra errors were introduced when preprocessing the Chinese documents, we can speculate that the SVM and the variants are quite robust to the noise in feature vectors for large categories but are sensitive to the data noise for small categories.

we had to segment Chinese sentence into words.


At the same time, the results of experiments are figured out and analyzed.

for example, the Construe [Church , K.W.Lisa.f.Rau 1995] is developed based on it.


The results judged by machine are regarded as correct, by means of artificial discrimination.

generally, feature weight is computed and normalized.


According to a study in cognitive science, people often disambiguate word sense using only a few other words in a given context (frequently only one additional word) [8] .

the blithesome collaboration with Dr. Ying Chenjin and Mr. Guo qingjian from Chinese Department is memorable for all of us.


We define an left-open edge, <w,v] of a GST as the edge that contains nodes between the upper node Lemma lc: Each substring that terminates in a node between a left-open edge <w, v] has the same tf and df.

a left-open edge <w, v] of the GST has a upper node w and lower node v. The substring "aca" that terminates in <w, v] has a counts of 3;2, whose occurrences are underlined in each of the strings in the GST.


The discussion in section 2.1 explain Property E can be achieved by defining domination range which classify the substrings in a GST into 0(N) classes in 0(N) time.

in addition, the time grows linearly with our corpus size.


In addition, the new child state will be the suffix state of the child state.

the proof of Property F for GSt is achieved by considering the following formulae: given a substring w = xyz, where x, xy, xyz are the longest substrings in their respective classes: the above formulae can be computed in constant time, since each of the involved in the formula can be accessed from root by traversing one of the substrings: w, y, xy and yz.


They are required in multigram compound discovery.

following that, we set the SID of the suffix state to j if the STD of the suffix state is less than j.


A similarity between two sentences of a language is a nonempty sequence of common items (root words or morphemes) in both sentences.

the SttL and DttL are combined as the template learning algorithm (ttL).


Then we use GIS algorithm to estimate parameters of all existing features in the model.

and every feature must encode a binary value that might help predicting the indexing result.


We have compared the results of ME model with the method of scoring by experience (Li 2003) and Chien's(1997) The result is not very satisfying.

the first are those which have rules of composition, mainly names and foreign terms.


Then we can make parameters more objective.

we can conclude that the fault doesn't lie in the ME model itself.


In our method, we didn't make use of any training corpus.

the detailed results are as follows: Precision for topi-k Extracted Ms 1 2 3 4 5 6 8 9 10 K x 1000) (Fig.


CLEs consist of 1,252 entries and 1,913 functional annotations for 675 words.

the Average is the average of the 200 averages for all parses for each sentence.


As described in Section 1, an f-structure is meant to encode a more language universal level of analysis.

then the lexical entry for each word in the input is automatically created (Sentence Lexical Entry: SLE).


The most similar word is chosen as the answer to the back-transliteration problem.

there is one further point which should not be ignored.


Concluding remarks are made in Section 5.

we use the similarity-based approach to model the task of back-transliteration.


Moreover, YC is derived from the extension of a chunk (hence extended chunk or EC) and is essentially a sentence (called EC sentence and represented as ErJ) in the sentence format (SF) of !31.

the English translation may or may not inherit the SC of the source sentence, despite the fact that it does, more often than not, take a different one.


This candidate is also out because it violates *RED, which, according to the ranking in Cantonese, is not allowed.

useless or redundant c-structure constituents should not be allowed.


However, the data of serial verb construction using manner verbs is small in our corpus.

one informant split the instructions into two parts.


The HPSG valence/content analysis is that the content of a case phrase structure-shares with a part of the content of its immediately larger constituent only through the valence of its head verb.

b's response to A's utterance in (2) sounds worse than that in (1).


See Kay 2002 for the meaning or function of a construction.

3 in Sag's analysis, a phantom relation corresponding to a verb (a relation of a verb not occurring but its meaning existent) is postulated in syntax or semantics.


And it may be the human nature to like and pursue the economy.

we recognize the semantic pattern of huaiyi sentences by its function, not by the meaning of huaiyi, which is not the fundamental basis of semantic recognition in speech communication."


Also the training result is usually hard for human beings to understand, thus it's relatively hard to integrate expert knowledge with the machine learning method.

if there're too many unknown categories, the document might be of another domain.


They employed literary texts written by foreign authors.

judging from these facts, it is very significan that the verb "fukumu (to include)" has 85 examples.


and Ck has direct relations with C. Here C and R indicate concepts and semantic relations, respectively.

his method differs from ours in that we consider all semantic relations in the ontology, not taxonomy relations only.


Note that in ZA detection rules, we employ the rule 1 to detect omitted cases as ZA candidates, and the rule 2 is an exceptional case which a verb appears in the initial position of an utterance but the verb is an intransitive verb tagged as VA or VH.

they are not constrained by features of any previous utterance in the discourse segment (DS), and the elements of CI(U") are partially ordered to reflect relative prominence in U,,.


To investigate this, I will make a distinction between segmental and tonal neighborhood.

next, the study demonstrated that within each category, defined by whether or not the stimuli obey the postulated voicing constraint and whether or not the clusters exist in the language, there were segmental neighborhood effects.


Moreover, another objective of the experiment is to obtain additional findings concerning segmental and tonal neighborhood effects on speakers' well-formedness judgments of novel words.

there exist a number of thai words that begin with the thai letter representing /t/ followed by the letter for they are not pronounced together as a cluster.


As has been observed in many studies, various functional relations are expressed by particles in Japanese.

phrase structure rules and constraints for focus particles are described in the grammar rules.


1985), the dependency between which man (the filler) and loves (the head that selects it) is usually mediated through an empty category (trace) located in the filler's "canonical" position.

obviously, theories with no movement operation (GPSG, LFG, HPSG, categorial grammars, etc.)


As narrated sequentially, these events took place without any overlap or inclusion.

the verb "ca" (sleep) is marked with the PAST tense marker "ss".


manner adverbs) restrict the range of events referred to; so that when such adverbs are selected by a verb, they contribute a necessary part of the information that the VP supplies in defining a given situation, just as an object selected by a transitive verb.

it appears difficult for such a functional account employing the paraphrasability to explain the difference between (23a) and (23b), where an adverb is in the sentence-initial position.


Just like the LICENSOR feature, it originates from a CA and acts as the syntactic "connector" of the CA and its correspondent VE.

the modifier-modified relationship can also be handled more easily once we have a syntactic mechanism connecting the two elements.


The sentence would have to be grammatical in whatever order the CAs are, as far as they are under the scope of their VEs.

there are two different types of unboundedness involved: one between a concord adverbial and a verbal ending and the other between the adverbial as a modifier and a predicate.


Then, we will propose a new feature LICENSEE for a better analysis of them.

far, we have considered the nature of the unbounded relationships in CACs.


Compare this with (6a) for the analysis of ta (perfect).

in (22b), there is no such involvement of a reference point, In (20c), the perfect state is introduced straightforwardly into the main DRS, and the triggering events are not introduced into the main DRS.


amedama-wa] candy-TOP tabe-tei-ru (-kedo...) eat-PROG -PRES -BUT 'It is (at least) the candy which the children are eating.

this is sufficient to identify the informational contribution of such a NP as focus.


In Section 3.2.1, we have formalized the constraint on topic which if present appears in sentence-initial position.

two options are available within the principle in (16): (i) accounts for instructions with narrow focus in (25a): it requires a mother to inherit the INFO-ST value of its daughters, as represented by the structure-sharing using 2 in (30a).


7 Another advantage of using the multi-dimensional representation concerns mismatches between informational partitioning and syntactic constituency.

we conclude that topic wa and (N)RF wa function as link and focus marker, respectively.


In this experiment, we used all sentences that incorporated positive instance noun-verb pairs to learn constructional and lexical preferences.

below, we outline the two methods, the first of which uses hand-generated templates to identify candidates for each role type, while the second employs a supervised learning technique to dynamically identify templates predictive of the different role types.


moreover, it costs a lot to develop parsers of both the source and target language.


The unknown word rates vary by datasets, CHTB has the most unknown words whereas AS has the least.

in the next row, we have shown that re-segmentaion using the disambiguation model improves the results, as those missing words (found in one context but not the other) can be corrected.


A separate process for unknown word detection could help to increase the lexicon and improve on the segmentation accuracy.

first, we use the unknown word model to extract unknown word candidates from the input text and apply a pruning process to them.


At first, people believed in rule based method, and tried to apply rules for word segmentation.

this is because SVM requires more computational power.


Let's take the person name " " (Sun yanzi) as an example.

if the unknown word rate is low, such as AS corpus, it would be better if all the detected words are used for re-segmentation because the pruning steps eliminate too many valid unknown words (5%) relatively.


Please note that the benchmark of random prediction among the three types of relations is 33.33%.

in addition, the internal relation of the VV compound has already been assigned to one of the three types: coordinate, modificational, and resultative.


2003), our work on Mandarin Chinese and the CTB to date uses a smaller feature set and a less fine-grained analysis.

a recent body of research had extended the basic paradigm of automatic PCFG acquisition from treebanks to the extraction of deep, wide-coverage, constraint-based grammars and lexical resources such as LFG (Cahill et al., 2002;Cahill et al., 2003;Cahill et al., 2004;O'Donovan et al., 2004), HPSG (Miyao et al., 2003;Miyao et al., 2004) and CCG (Hockenmaier and Steedman, 2002;Hockenmaier, 2003).


The method is found to require a huge search space, consuming far more computation time than we could afford.


But if the system is designed to work at the syllable level, these linking syllables can be generated from a specific syllable in a certain context.


Space is inserted as word separation, such as "prasop khwam samret" (meet -Nom.

we believe that syllabification is a necessary step for grapheme-to-phoneme conversion.


In the following discussion, I will argue that shi-support is needed in order to satisfy the ECP (Empty Category Principle) requirement because of the intervening barriers 6 .

if we cannot see the difference between them, how can we further account for the asymmetry between these two different kinds of wh-phrases?


Such movement is more economical, and the mysteries of this argument-adjunct asymmetry will be revealed, as shown in 7and 8 Zhangsan like someone but I not know be who ' Z h a n g s a n l i k e s someone, but I don ' t k n o w wh o ' shi exists before the wh-argument Shei ' Wh o ' is that the trace of this wh-argument cannot be antecedent-governed by its antecedent in the lower hierarchical level, and thus must be lexically-governed or head-governed by a lexical head, such as the preceding verb Zhidao ' k n o w . '

zhangsan like Meili ' z h a n g s a n l i k e s Me i l i . '


Different from their analysis in crucial respects, however, I argued that a covert wh-movement also takes place simultaneously with the operation of copying the a n t e c e d e n t I P .

since shi-support is a language-particular phenomenon, it only functions as the last resort to rescue the ill-formed Sluicing sentences.


Departing from their analysis in crucial respects, however, I argue that a covert wh-movement also takes place simultaneously with the operation of copying the antecedent IP.

we have nothing but to pursue another lexical head.


cut and kiru, as main verbs, both express division sense such as in cut a cake and keeki-o kiru 'to cut a cake'.


Both creation of concrete artifacts and performance of events are secondary senses derived from co-composition.

it also has its own function called type coercion, which is unpredictable from the semantics of the main verb.


A target-language term should have pronunciation as close to the source-language transliterated equivalent as possible.

this algorithm establishes a conceptual foundation for term transliteration.


200 sentences were separated at random as the test set for unseen texts.

travel routes) in a choice set and "chooses" one route.


Examples: (every 5th and 10th day), (door-to-door), (mimetic word; fast), gotoobi doatsuudoa byuun (access), (annoying) akusesu iratsuku A collection of complex words means the combination of nouns, verbs, particles, etc.

inui (2004) analyzed open-ended questionnaire texts about traffic infrastructure planning, focusing on the intention behind answers.


Rather, " (fast)" is recognized, and " " only modifies " ."

for example, the cognition result "time" can be extracted from the sentence "Waiting takes time."


Details will be reported in another paper.

when developing new products, a company must analyze customers' awareness from a bottom-up viewpoint.


Statistically acquiring sufficient knowledge about a language to build a robust WSD system is extremely difficult.

generality and adaptability are, therefore, essential to a robust and portable WSD system.


So far over 10 million character of Chinese texts of different types and corresponding English texts has been collected and included into the corpus.

users can know "发扬" and "民主" co-occurred 5 times in the small sub-corpus loaded by the concordance tool.


Most of Chinese words do not have morphological variants.

2) Monolingual textual structural annotation.


BAI Xiaojing and ZHAN Weidong (2003) explored how English passive form could be translated into Chinese, either with Chinese preposition " 被" or not.

in a segmented corpus, both "民主" and "民主集中制 2 " are segmented as word.


These recognition results are fed into the computer scoring system.

the responses to these two parts allow score uses or test administrators to listen to test-taker's spontaneous speech.


"approach" and "length" were transformed into "size" and "way", respectively.

• For words with the same number of instances of "l" and "r" preceding vowels, a word PACLIC 18, December 8th-10th, 2004, Waseda University, Tokyo Table 2: Examples of transforming English sentences into sentences containing fewer instances of "l" and "r" letters preceding vowels Correct transformation We think a good approach way is to construct it using "X no Y".


However, LVCs differ from idiomatic phrases in that, in most cases, their semantics (and hence their translation) is more predictable.

hence, deciding precisely which light verbs combine with which nouns to form acceptable LVCs is a difficult task.


Take a lunch is a compositional combination of a verb and a noun, both contributing their literal semantics.

due to the peculiarities in the behaviour of metaphorical and idiomatic expressions, they have been mostly overlooked by researchers in computational linguistics.


Example 16 (b) shows a new sentence produced by reordering words of the sentence in (a).

the more complex case in Example 9 (a) can be represented in sign language by moving the right hand shaped for 'to see' to the right-hand side of the speaker, and the left hand also shaped for 'to see' to the left-hand side of the speaker.


However, expressions in sign language represent their syntactic information by varying word order rather than by varying words themselves, which is the very reason why they are classified as an isolating language.

this additional representation further clarifies the sense as shown in (c).


Phrases composed of Chinese characters are considered as unknown words.

example 11 (a) 어머니께서 나에게 빨래를 하라고 하셨다.


We restrict the scope of Korean sentences to be practical and concrete, avoiding the use of words beyond everyday usage, and the use of sentences with highly complex structures.

it is desirable to rephrase unknown words in an explanatory manner or to express them with the use of fingers for exact spelling.


These tables show percentages  according to the syntactic positions of 'market.'

chinese speakers say 'market' as above the market rather than inside the market.


For example, the noun root halatra 'theft' is specified with the continuation class NTR2RWeak.

we also make use of P-type and R-type flag diacritics to model long distance dependencies between verbal and nominal affixes.


For example, if we decided to implement the lexical preference for passive suffixation in -ina rather than -ana as a feature, the lexical entry for fantatra in the lexicon would be accompanied by a featureˆFpassi on the surface level as bellow.

as with any finite-state morphological transducer, our Malagasy morphological analyzer is bidirectional: it can be used in grammatical analysis to produce morphologically analyzed input to a parser, or in generation to produce a surface form from a specification of lexical properties (Beesley and Karttunen, 2003).


Three-syllable roots take penultimate stress unless they end in one of the 'weak syllables' na/ny, ka, tra, in which case they usually receive antepenultimate stress and are called 'weak roots' (Keenan and Polinsky, 1998).

because they do not take effect until the interpretation phase they do not interfere with the XFST rules.


They preferred the non-solids for the shape match non-solid trials, selecting it 94% of the time.

virtually no studies examined this distinction in classifier languages which supposedly lack the distinction.


However, the children's pattern for shape match non-solid trials differed from adults; both groups of children were willing to pick the non-solids that matched the shape.

we tested six count classifiers: gen (rod), zhi (stick), tiao (line), zhang (sheet), pian (slice), and kuai (chunk).


Virtually no studies examined this distinction in classifier languages which supposedly lack the distinction.

the closed box was correct for two of these trials.


Accordingly, we propose a sequential parser that determines syntactic structure based on semantic analysis referring to the context and on relevant statistical information.

dependency structures containing them can be transformed into the form "an attribute is a value" in Figure 2.


Homographs are traditionally regarded as a special case of word sense disambiguation problem, and are usually addressed by corpus based statistical methods.

examples (16) and (8) are expressed with the same predicate '싫어한다(silhehanta: dislike)', but the presence of the unconjugated adjectives for the object '밤(pam)' in example (16) helps restrict the sense of the homographic noun to 'night' with short vowel sound.


No feedback was given to correct responses.

by item analysis, passives were rated less grammatical than canonical variants for simple sentences (t(23) = 3.07, p < 0.01), less grammatical than both canonical and ba variants for sentences with RCs (t(23) = 5.68, p < 0.001; t(23) = 2.27, p < 0.05), and less grammatical than canonical variants for sentences with adjunct RCs (t(23) = 4.92, p < 0.001).


In (17), we observe that Skolem term dareka 'somebody' allows only the narrow scope reading.

( -1976) "Pivot-Independent Relativization in Japanese II," Reprinted in S.-Y.


the transitive verb loves has the following category with its environment being the empty set: 5 (12) Application of a rule induces environment passing in the following way: if a function with environment F is applied to an argument with environment A , the environment of the argument in the resulting LF is the union of the two (F U A).


Using a corpus, statistical-based MT allows flexibilty to accomodate different domains.

the evaluator used the following rating scheme: 1 -Unacceptable, 2 -Poor, 3 -Acceptable, 4 -Good, and 5 -Accurate.


Viewpoints of the observer do not seem to play an important role in in and out orientations.

nei4 and wai4 are symmetric as in (1a)-(1d): guo2nei4 國內 'inside the country; domestic'/guo2wai4 國外 'outside the country; abroad'; nei4meng2 內 蒙 'Inner Mongolia'/ wai4meng2 外蒙 'Outer Mongolia'; nei4xiao1 內銷 'sell domestically'/wai4xiao1 外銷 'export', etc.


This observation suggests that the choice and the distribution of referring expressions in the task-oriented dialogues depends on the way the participant collaborate to judge the most salient entity in the current discourse against their common ground.

based on Grosz, Joshi and Weinstein (1995) and Walker, Joshi and Prince (1998), I adopt this ranking in analyzing the data.


For example, a verbal stem '먹 mek' may simply concatenate with a tense marker to form another stem, but its syllable structure may restrict the choice of its ending, thus allowing the stem '먹었 mek.ess' only.

the end of verbalEnding entries adnoun = [ 추운 "@P.SURF.


Most IE researches are concerned with producing better performance and facilitating development portability [2][5] [1].

these common string features may be found at the string's surface form level.


Given a set of n-tuple, r = {t 1 , t 2 , …, t m }, in the database initially loaded by an IE task, we will apply the set of string features to the set of n-tuple such that each value v i in the database is evaluated by six string features.


A perfect classifier will have true positive and true negative rates of 1.

the accuracy of detecting invalid data also depends on the boundary between majority and minority.